This study evaluates two approaches applied to computed tomography (CT)
images of patients with abdominal aortic aneurysm: one deterministic, based on
tools of Approximation Theory, and one based on Artificial Intelligence. Both
aim to segment the basal CT images to extract the patent area of the aortic
vessel, in order to propose an alternative to nephrotoxic contrast agents for
diagnosing this pathology. While the deterministic approach employs sampling
Kantorovich operators and the theory behind, leveraging the reconstruction and
enhancement capabilities of these operators applied to images, the artificial
intelligence-based approach lays on a U-net neural network. The results
obtained from testing the two methods have been compared numerically and
visually to assess their performances, demonstrating that both models yield
accurate results.

本研究评估了应用于腹主动脉瘤患者计算机断层扫描图像的两种方法：一种基于近似理论工具的确定性方法和一种基于人工智能的方法。两种方法均旨在分割基底 CT 图像，以提取主动脉血管的患区，为诊断该病理提供尿毒症造影剂的替代方案。实验结果定量和定性地比较了这两种方法的性能，表明两种模型均能得出准确的结果。